1. Field of the Invention
The present invention relates in general to a method and system for converting gray-scale images to binary images which employs fuzzy reasoning to enhance a gray-scale image before the image is converted to a binary image. When the enhanced gray-scale image is converted to a binary image, the binary image retains more of the detailed information that was contained in the original gray-scale image.
2. Description of the Background Art
Binarization of a gray-scale/color image is a fundamental tool for image processing since a binary image (1-bit image) can be processed with very fast logical (Boolean) operators and also be used to digitize text and numbers originally contained in gray-scale/color images (typically 8-bit images). The binary digitization of text and numbers contained in gray-scale images has a wide application that includes, but is not limited to, Object Character Recognition (OCR). In a binary digital image, a binary one value indicates that the pixel belongs to the image foreground, which may represent an object in the image, while a binary zero value indicates that the pixel is darker and belongs to the image's background. Since most image display systems and software employ gray-scale images of 8 or more bits per pixel, the binarization of these images usually takes 2 extreme gray tones, black and white, which are ordinarily represented by 0 or 255, respectively, in an 8-bit gray-scale display environment.
One of the key goals of performing binarization of gray-scale images is to be able to digitize text and numbers contained in a gray-scale image acquired outdoors where the image acquisition conditions (lighting, camera/video stabilization, weather, etc.) are uncontrolled. Due to the physical properties of the imaging devices and due to the image transmission, these uncontrolled conditions can result in the degradation of the gray-scale images such that they have poor contrast, are corrupted with different kinds of noise or are blurred. As a result, it is often more difficult to binarize the text and numbers contained in such images; for example, the binarization and final digitization of a car's license tag numbers face these problems, thus making their binarization/digitization unfeasible or unreliable.
Various Image enhancement techniques are available that are utilized to increase the contrast, smooth the regions of interest and sharpen the edges and fine structures in the images. These techniques comprise different classes of point and spatial operations and are typically selected in accordance with the ultimate purpose on the outputted image and the quality criterion (or criteria) of the original (input) image. To date, however, no known enhancement technique exists for specifically improving the binarization/digitization of a gray-scale image so that certain features, such as text or numbers, remain discernable in the image after binarization.